JP7140551B2 - Magnetic resonance imaging apparatus, processing apparatus, and medical image processing method - Google Patents

Description

TECHNICAL FIELD Embodiments of the present invention relate to a magnetic resonance imaging apparatus, a processing apparatus, and a medical image processing method.

2. Description of the Related Art In recent years, techniques for acquiring time-series images such as dynamic imaging have been known in magnetic resonance imaging (MRI) apparatuses.

Specifically, for example, there is a method of reconstructing a moving image from k-space data acquired by radial scanning using NFFT (Nonequispaced Fast Fourier Transform). The technique reduces the amount of k-space data to be collected and shortens the imaging time by sparsely collecting the peripheral regions of k-space (ie, below the Nyquist sampling criteria). The collected k-space data supplements the information corresponding to the peripheral region of the k-space by regularization or the like at the time of reconstruction. However, this method may lose the sharpness of contour lines in the image because the interpolation accuracy of the peripheral region of k-space is low.

Also, for example, k There is a method of shortening the imaging time by subtracting the spatial average data. However, this method takes advantage of the property that the same location on the image is acquired multiple times, so it is not applicable to radial scanning using the golden angle method, which does not acquire the same location on k-space multiple times. is difficult.

In either method, the imaging time can be shortened so as to satisfy the restricted processing time, but the image quality may be degraded. That is, with the conventional method, it is difficult to improve the image quality of the magnetic resonance image within the restricted processing time.

U.S. Patent Application Publication No. 2005/0174113

Ｌｉ Ｆｅｎｇ、外８名、“Ｇｏｌｄｅｎ－Ａｎｇｌｅ Ｒａｄｉａｌ Ｓｐａｒｓｅ Ｐａｒａｌｌｅｌ ＭＲＩ： Ｃｏｍｂｉｎａｔｉｏｎ ｏｆ Ｃｏｍｐｒｅｓｓｅｄ Ｓｅｎｓｉｎｇ， Ｐａｒａｌｌｅｌ Ｉｍａｇｉｎｇ， ａｎｄ Ｇｏｌｄｅｎ－Ａｎｇｌｅ Ｒａｄｉａｌ Ｓａｍｐｌｉｎｇ ｆｏｒ Ｆａｓｔ ａｎｄ Ｆｌｅｘｉｂｌｅ Ｄｙｎａｍｉｃ Ｖｏｌｕｍｅｔｒｉｃ ＭＲＩ”、Ｍａｇｎｅｔｉｃ Ｒｅｓｏｎａｎｃｅ ｉｎ Ｍｅｄｉｃｉｎｅ ７２：７０７－７１７、２０１４ Year

The problem to be solved by the present invention is to improve the quality of magnetic resonance images in a limited processing time.

A magnetic resonance imaging apparatus according to an embodiment includes an inverse transformation unit, a generation unit, a difference unit, a reconstruction unit, and a synthesis unit. The inverse transform unit generates reference k-space data by applying the inverse transform of the reconstruction process to the reference image. The generation unit generates a plurality of reference portions based on the filling positions and the reference k-space data so that the filling positions of the data in the k-space are different and correspond to the plurality of partial k-space data relating to the plurality of frames along the time series. Generate k-space data. The differencing unit generates a plurality of differential k-space data by performing a difference between the plurality of partial k-space data and the plurality of reference k-space data for each of the plurality of frames. The reconstruction unit generates a plurality of differential images by applying reconstruction processing to each of the plurality of differential k-space data. The synthesis unit generates a plurality of synthesized images by synthesizing the reference image and each of the plurality of difference images.

1 is a diagram illustrating a configuration example of an MRI apparatus according to an embodiment; FIG. FIG. 2 illustrates k-space data collected along all spokes by radial scanning, in one embodiment. 3 is a diagram illustrating first partial k-space data thinned out from the k-space data of FIG. 2; FIG. 4 is a diagram illustrating second partial k-space data thinned out from the k-space data of FIG. 2; FIG. 5 is a diagram illustrating third partial k-space data thinned out from the k-space data of FIG. 2; FIG. FIG. 6 is a diagram illustrating a collection order of data corresponding to spokes collected by radial scanning in one embodiment. FIG. 7 is a diagram showing a correspondence table in which frames and partial k-space data are associated with each other in one embodiment; FIG. 8 is a flowchart illustrating processing performed by a processing circuit in one embodiment; FIG. 9 is a diagram illustrating a specific example of reconstruction processing in one embodiment;

Hereinafter, embodiments of a magnetic resonance imaging apparatus, a processing apparatus, and a medical image processing method will be described in detail with reference to the drawings.

FIG. 1 is a diagram showing a configuration example of an MRI apparatus according to one embodiment. For example, as shown in FIG. 1, the MRI apparatus 100 according to one embodiment includes a static magnetic field magnet 101, a gradient magnetic field coil 103, a gradient magnetic field power supply 105, a bed 107, a bed control circuit 109, and a transmission circuit 113. , a transmission coil 115 , a reception coil 117 , a reception circuit 119 , an imaging control circuit 121 , an interface 123 , a display 125 , a storage device 127 , and a processing circuit 129 . The MRI apparatus 100 may have a hollow cylindrical shim coil between the static magnetic field magnet 101 and the gradient magnetic field coil 103 .

The static magnetic field magnet 101 is, for example, a magnet formed in a hollow, substantially cylindrical shape. The static magnetic field magnet 101 generates a uniform static magnetic field in a bore 111, which is a space into which the subject P is inserted. A superconducting magnet, for example, is used as the static magnetic field magnet 101 .

The gradient magnetic field coil 103 is, for example, a coil formed in a hollow, substantially cylindrical shape. The gradient magnetic field coil 103 is arranged inside the static magnetic field magnet 101 . The gradient magnetic field coil 103 is formed by combining three coils corresponding to the mutually orthogonal X, Y, and Z axes. It is assumed that the Z-axis direction is the same as the direction of the static magnetic field. The Y-axis direction is the vertical direction, and the X-axis direction is the direction perpendicular to the Z-axis and the Y-axis. The gradient magnetic field coil 103 generates a gradient magnetic field to be superimposed on the static magnetic field. Specifically, the three coils in the gradient magnetic field coil 103 are individually supplied with current from the gradient magnetic field power supply 105 to generate gradient magnetic fields whose magnetic field strengths vary along the X, Y, and Z axes.

The gradient magnetic fields along the X, Y, and Z axes generated by the gradient magnetic field coil 103 form, for example, a frequency encoding gradient magnetic field (also referred to as a readout gradient magnetic field), a phase encoding gradient magnetic field, and a slice selection gradient magnetic field. . A frequency-encoding gradient magnetic field is used to vary the frequency of the MR signal according to spatial location. A phase-encoding gradient magnetic field is used to change the phase of a magnetic resonance (Magnetic Resonance: MR) signal according to a spatial position. The slice selection gradient magnetic field is used to determine the imaging section.

The gradient magnetic field power supply 105 is a power supply device that supplies current to the gradient magnetic field coil 103 under the control of the imaging control circuit 121 .

The bed 107 is an apparatus having a top board 107a on which the subject P is placed. The bed 107 inserts the tabletop 107a on which the subject P is placed into the bore 111 under the control of the bed control circuit 109 . The bed 107 is installed, for example, in the examination room where the MRI apparatus 100 is installed so that the longitudinal direction is parallel to the central axis of the static magnetic field magnet 101 .

A bed control circuit 109 is a circuit that controls the bed 107 . The bed control circuit 109 drives the bed 107 according to an operator's instruction via the interface 123 to move the table top 107a in the longitudinal direction, the up-down direction, and in some cases the left-right direction.

The transmission circuit 113 supplies a high-frequency pulse corresponding to the Larmor frequency or the like to the transmission coil 115 under the control of the imaging control circuit 121 .

The transmission coil 115 is an RF (Radio Frequency) coil arranged inside the gradient magnetic field coil 103 . The transmission coil 115 receives a high frequency pulse from the transmission circuit 113 and generates a transmission RF wave (RF pulse) corresponding to a high frequency magnetic field. The transmission coil is, for example, a whole body coil (WB coil). A WB coil may be used as a transmit/receive coil.

The receiving coil 117 is an RF coil arranged inside the gradient magnetic field coil 103 . The receiving coil 117 receives MR signals emitted from the subject P by the high frequency magnetic field. Receiving coil 117 outputs the received MR signal to receiving circuit 119 . The receiving coil 117 is, for example, one or more coils, typically a coil array having a plurality of coil elements. It should be noted that although transmit coil 115 and receive coil 117 are depicted as separate RF coils in FIG. 1, transmit coil 115 and receive coil 117 may be implemented as an integrated transmit and receive coil. The transmit/receive coil is, for example, a local transmit/receive RF coil, such as a head coil.

The receiving circuit 119 generates a digital MR signal, which is digitized complex number data, based on the MR signal output from the receiving coil 117 under the control of the imaging control circuit 121 . Specifically, the receiving circuit 119 performs various signal processing on the MR signal output from the receiving coil 117, and then performs analog/digital (A/D) processing on the data subjected to the various signal processing. to Digital)) Perform conversion. The receiving circuit 119 samples the A/D converted data to generate a digital MR signal (hereinafter referred to as MR data). The receiving circuit 119 outputs the generated MR data to the imaging control circuit 121 .

The imaging control circuit 121 controls the gradient magnetic field power supply 105, the transmission circuit 113, the reception circuit 119, etc. according to the imaging protocol output from the processing circuit 129, and performs imaging of the subject P. FIG. The imaging protocol has various pulse sequences according to examinations. The imaging protocol includes the magnitude of the current supplied to the gradient magnetic field coil 103 by the gradient magnetic field power supply 105, the timing at which the current is supplied to the gradient magnetic field coil 103 by the gradient magnetic field power supply 105, and the current supplied to the transmission coil 115 by the transmission circuit 113. The magnitude of the high-frequency pulse, the timing at which the transmission circuit 113 supplies the high-frequency pulse to the transmission coil 115, the timing at which the reception coil 117 receives the MR signal, and the like are set in advance. The imaging control circuit 121 is an example of an acquisition unit.

For example, when radial scanning is performed by a pulse sequence in which MR signals are acquired in each of a plurality of acquisition lines along a plurality of radial directions in the k-space, the imaging control circuit 121 uses a phase encoding gradient as a readout gradient magnetic field. The gradient magnetic field power supply 105 is controlled so as to simultaneously generate a magnetic field and a gradient magnetic field for frequency encoding. In addition, the imaging control circuit 121 controls the gradient magnetic field power supply 105 so as to change the strength of the phase-encoding gradient magnetic field and the strength of the frequency-encoding gradient magnetic field each time a high-frequency pulse is applied to the transmission coil 115. . The imaging control circuit 121 controls the receiving circuit 119 to receive the MR signal along with the generation of the readout gradient magnetic field. The imaging control circuit 121 outputs the MR data output from the receiving circuit 119 to the processing circuit 129 and the storage device 127 together with the strength of the readout gradient magnetic field and the radial direction (position filled in the k-space).

In the radial scan, scanning is performed through the center of the k-space while rotating the reading direction on the cross-section arranged to pass through the center of the k-space. By performing a radial scan, two-dimensional data inside a circle drawn on a cross-section of k-space can be collected. That is, the radial scan acquires a central region of k-space with high density and a peripheral region of k-space with low density. In the radial scan, the shape of the acquisition line radially extending from the center of the k-space is likened to a wheel, and the line segment drawn by scanning in the reading direction is called a spoke.

Unlike the Cartesian scan, the radial scan does not necessarily obtain sample values (signal values) corresponding to grid points on the k-space. Therefore, reconstruction of data collected by radial scanning uses a technique different from Fourier transform (or inverse discrete Fourier transform). Reconstruction of data collected by radial scanning models the process of generating signals from MR images using, for example, Nonequispaced Discrete Fourier Transform (NDFT) or Nonequispaced Fast Fourier Transform (NFFT), which is a fast version of the NDFT. and solving the inverse problem of the model. Both NDFT and NFFT are linear transforms. If the number of samples collected by radial scanning is sufficiently large, a method called gridding may be used for data reconstruction.

In addition, the reconstruction image quality can be improved by using parallel imaging (PI) in combination. PI is a technique of reconstructing an image from data obtained by scanning a part of k-space data (partial k-space data) using sensitivity differences of a plurality of receiving coils.

Techniques for generating MR images from partial k-space data are used, for example, in dynamic imaging. Dynamic imaging is the continuous imaging of a plurality of MR images. When the change in the subject appears only in a part of the MR image, it is possible to reconstruct the MR image from k-space data (partial k-space data) with a reduced amount of data acquisition by considering continuity in the time direction. It is possible.

Compressed sensing, for example, is known as a reconstruction technique that considers continuity in the time direction. Compressed sensing assumes that when a known linear transformation is applied to input data, most of the values of the applied data are zero. It is a technique to estimate the original input data by using In one embodiment, for example, if a linear transformation is applied to a reconstructed image with respect to frame-to-frame differences in pixel values, the assumption is introduced that most of the pixel values in the applied reconstructed image are zero. A “frame” is an individual still image that constitutes a moving image, and corresponds to, for example, a reconstructed MR image.

Specifically, the imaging control circuit 121 acquires a plurality of partial k-space data relating to a plurality of time-series frames with different data filling positions in the k-space. “Different data filling positions in k-space” means, for example, in radial scanning, data are collected on spokes at different positions between adjacent frames. "Partial k-space data" means, for example, k-space data corresponding to each of a plurality of frames, in which spoke filling positions are thinned out. That is, the data collection amount of the partial k-space data is a data collection amount smaller than the data collection amount that can originally be acquired.

Note that the imaging control circuit 121 may separately acquire k-space data for generating a reference image before acquiring a plurality of partial k-space data. Also, the imaging control circuit 121 may separately acquire k-space data for generating a reference image after acquiring a plurality of partial k-space data. Further, the imaging control circuit 121 may separately acquire k-space data for generating a reference image before or after a contrast examination. Details of the reference image will be described later.

The total number of spokes in k-space is arbitrarily determined. For example, if the total number of non-overlapping spokes is set to 600 and signals are collected from 20 spokes per frame, then 30 frames of partial k-space data with non-overlapping spokes between frames, can be obtained. The number of spokes included in the partial k-space data is smaller than the total number of spokes set on the k-space. Also, in the partial k-space data corresponding to adjacent frames, the spokes are arranged differently between frames.

Also, in radial scanning, only the number of spokes per frame may be determined. In this case, in order to set adjacent spokes so that they do not overlap, spokes passing through the origin in k-space and increasing in increments of 55.6 degrees are used, or spokes are increased in increments of 111.25 degrees called the golden angle method. You can use spokes.

FIG. 2 is a diagram illustrating k-space data collected along all spokes by radial scanning, in one embodiment. For example, as shown in FIG. 2, k-space data 200 according to one embodiment is acquired at acquisition positions along each readout direction from spoke S1 to spoke S12. In FIGS. 2 to 7, the total number of spokes in the k-space data is 12 for simplicity of explanation, but the number is not limited to this.

FIG. 3 is a diagram illustrating first partial k-space data decimated from the k-space data of FIG. For example, as shown in FIG. 3, the first partial k-space data 300 is acquired at acquisition positions along the reading direction of each of the spokes S1, S4, S7, and S10. In other words, the first partial k-space data 300 is k-space data composed of a combination of spokes S1, S4, S7 and S10.

FIG. 4 is a diagram illustrating second partial k-space data decimated from the k-space data of FIG. For example, as shown in FIG. 4, second partial k-space data 400 is acquired at acquisition positions along the readout direction of spokes S2, S5, S8, and S11. In other words, the second partial k-space data 400 is k-space data composed of a combination of spokes S2, S5, S8 and S11. That is, the second partial k-space data 400 differs from the first partial k-space data 300 in the data filling positions in k-space.

FIG. 5 is a diagram illustrating third partial k-space data decimated from the k-space data of FIG. For example, as shown in FIG. 5, the third partial k-space data 500 is acquired at acquisition positions along the readout direction of each of the spokes S3, S6, S9, and S12. In other words, the third partial k-space data 500 is k-space data composed of a combination of spokes S3, S6, S9 and S12. That is, the third partial k-space data 500 differs from the first partial k-space data 300 and the second partial k-space data 400 in the data filling positions in k-space.

FIG. 6 is a diagram illustrating the acquisition order of data corresponding to spokes acquired by radial scanning in one embodiment. Hereinafter, the data corresponding to the spoke Sn will be referred to as "Sn data". For example, as shown in FIG. 6, k-space data corresponding to the first, second, and third frames are arranged in chronological order.

The k-space data used in the first frame are collected in the order of S1 data, S4 data, S7 data, and S10 data. The k-space data used in the second frame are collected in the order of S2 data, S5 data, S8 data, and S11 data. Furthermore, the k-space data used in the third frame are collected in the order of S3 data, S6 data, S9 data, and S12 data.

The boundary between the first frame and the second frame corresponds to the boundary between the data of S10 and the data of S2. The boundary between the second frame and the third frame corresponds to the boundary between the data of S11 and the data of S3.

Note that the order of data collection within a frame is not limited to the above and may be set freely. Therefore, the boundary between adjacent frames is not limited to the above, and corresponds to the boundary between adjacent data according to the data collection order.

FIG. 7 is a diagram showing a correspondence table that associates frames with partial k-space data in one embodiment. For example, as shown in FIG. 7, the correspondence table 700 associates the first frame with the first partial k-space data, associates the second frame with the second partial k-space data, and associates the third partial k-space data with the third frame. A frame is associated with the third partial k-space data. A Look Up Table (LUT) may be used as the correspondence table 700 .

The interface 123 has a circuit for receiving various instructions and information input from the operator. The interface 123 has circuitry relating to, for example, a pointing device such as a mouse, or an input device such as a keyboard. Note that the circuits included in the interface 123 are not limited to circuits related to physical operation parts such as a mouse and keyboard. For example, the interface 123 receives an electric signal corresponding to an input operation from an external input device provided separately from the MRI apparatus 100, and an electric signal processing circuit that outputs the received electric signal to various circuits. may have

The display 125 displays various MR images generated by the image generation function 129b and various information on imaging and image processing under the control of the system control function 129a in the processing circuit 129. FIG. Display 125 is a display device such as, for example, a CRT display, liquid crystal display, organic EL display, LED display, plasma display, or any other display or monitor known in the art.

The storage device 127 stores MR data filled in the k-space via the image generation function 129b, image data generated by the image generation function 129b, and the like. The storage device 127 stores various imaging protocols, imaging conditions including a plurality of imaging parameters defining the imaging protocols, and the like. The storage device 127 stores programs corresponding to various functions executed by the processing circuit 129 .

The storage device 127 may also store a combination of spokes associated with each of the frames. Specifically, the storage device 127 stores a correspondence table that associates frames with partial k-space data.

The storage device 127 is, for example, a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk drive, a solid state drive, an optical disk, or the like. Also, the storage device 127 may be a drive device that reads and writes various information from and to a portable storage medium such as a CD-ROM drive, a DVD drive, and a flash memory.

The processing circuit 129 has hardware resources such as a processor (not shown) and memories such as ROM (Read-Only Memory) and RAM, and controls the MRI apparatus 100 . The processing circuit 129 has a system control function 129a, an image generation function 129b, a reference k-space data generation function 129c, a reference partial k-space data generation function 129d, a differential k-space data generation function 129e, a reconstruction function 129f, and a composite image generation function. It has 129g. Performed by the system control function 129a, the image generation function 129b, the reference k-space data generation function 129c, the reference partial k-space data generation function 129d, the difference k-space data generation function 129e, the reconstruction function 129f, and the composite image generation function 129g. Various functions are stored in the storage device 127 in the form of programs executable by a computer. The processing circuit 129 is a processor that reads programs corresponding to these various functions from the storage device 127 and executes them, thereby implementing functions corresponding to each program. In other words, the processing circuit 129 in a state where each program is read has a plurality of functions shown in the processing circuit 129 of FIG.

In FIG. 1, it is assumed that the single processing circuit 129 implements the various functions described above, but the functions may be implemented by a plurality of independent processors executing programs. In other words, the various functions described above may be configured as programs, and one processing circuit may execute each program, or a specific function may be implemented in a dedicated, independent program execution circuit. good too.

The term "processor" used in the above description includes, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (for example, a simple Circuits such as Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs).

The processor implements various functions by reading and executing programs stored in the storage device 127 . Note that instead of storing the program in the storage device 127, the program may be configured to be directly installed in the circuit of the processor. In this case, the processor implements its functions by reading and executing the program embedded in the circuit. The bed control circuit 109, the transmission circuit 113, the reception circuit 119, the imaging control circuit 121, and the like are similarly configured by electronic circuits such as the processor. The processing circuit 129 also has a system control function 129a, an image generation function 129b, a reference k-space data generation function 129c, a reference portion k-space data generation function 129d, a difference k-space data generation function 129e, a reconstruction function 129f, and a composite image. The generation function 129g is an example of a system control unit, an image generation unit, an inverse transformation unit, a generation unit, a difference unit, a reconstruction unit, and a synthesis unit, respectively.

The processing circuitry 129 controls the MRI apparatus 100 with a system control function 129a. Specifically, the processing circuit 129 reads out the system control program stored in the storage device 127, expands it on the memory, and controls each circuit of the MRI apparatus 100 according to the expanded system control program. For example, the processing circuit 129 reads the imaging protocol from the storage device 127 based on the imaging conditions input by the operator via the interface 123 using the system control function 129a. Note that the processing circuitry 129 may generate an imaging protocol based on imaging conditions. The processing circuit 129 transmits an imaging protocol to the imaging control circuit 121 and controls imaging of the subject P. FIG.

The processing circuitry 129 fills the k-space with MR data by means of an image generation function 129b. The processing circuit 129 generates an MR image by performing, for example, Fourier transform on the MR data filled in the k-space. The MR image corresponds to a morphological image of the subject P, for example. The processing circuit 129 outputs the MR image to the display 125 and storage device 127 .

The processing circuit 129 generates reference k-space data by applying the inverse transformation of the reconstruction processing to the reference image using the reference k-space data generation function 129c. The inverse transformation of reconstruction processing corresponds to, for example, simulating and generating k-space data from a reference image. When simulating k-space data obtained by radial scanning, for example, k-space data is generated by using NDFT or NFFT on a reference image. The reference image is generated by reconstruction function 129f, but may also be generated by image generation function 129b.

A reference image is, for example, an image relating to k-space data obtained by integrating all of a plurality of partial k-space data corresponding to a plurality of frames. The plurality of frames are, for example, all frames for creating a moving image of a preset total number of frames. Every frame corresponds, for example, to k-space data acquired from different spokes for each frame.

Also, the reference image may be an image related to k-space data obtained by integrating at least two pieces of partial k-space data corresponding to a plurality of frames. Also, the reference image may be an image relating to k-space data acquired either before or after acquiring a plurality of partial k-space data corresponding to a plurality of frames. Also, the reference image may be an image relating to k-space data acquired before or after a contrast examination.

The processing circuit 129 uses a reference partial k-space data generation function 129d to generate filling positions and reference data so that the data filling positions in the k-space are different and correspond to a plurality of partial k-space data relating to a plurality of frames along the time series. A plurality of reference partial k-space data are generated based on the k-space data. Specifically, processing circuitry 129 generates a plurality of reference partial k-space data by extracting k-space trajectories respectively corresponding to fill locations from the reference k-space data.

The processing circuit 129 generates a plurality of differential k-space data by executing a difference between a plurality of partial k-space data and a plurality of reference k-space data for each of a plurality of frames by a differential k-space data generation function 129e. Generate.

The processing circuit 129 generates a plurality of difference images by applying a reconstruction process to each of the plurality of difference k-space data by the reconstruction function 129f. The processing circuit 129 may also generate a plurality of difference images by introducing correlations between a plurality of frames into the reconstruction process by the reconstruction function 129f. Note that the correlation between a plurality of frames corresponds to, for example, compressed sensing that considers continuity in the time direction.

The processing circuitry 129 may also generate a reference image by applying reconstruction processing to the k-space data using the reconstruction function 129f. Specifically, the processing circuit 129 generates a reference image by applying reconstruction processing to k-space data obtained by integrating all of the plurality of partial k-space data. When applying reconstruction processing to k-space data obtained by radial scanning, a reference image is generated by using, for example, NDFT or NFFT and compressed sensing on the reference image.

In addition, the processing circuit 129 uses the reconstruction function 129f to apply reconstruction processing to k-space data obtained by integrating at least N (N=2) or more of the plurality of partial k-space data. An image may be generated. The value of N is set so that the image quality of the reference image after reconstruction processing is ensured to some extent. For example, the processing circuitry 129 may determine the value of N when the noise level in the reference image falls below a threshold.

Further, the processing circuit 129 may generate a reference image by applying reconstruction processing to the k-space data acquired before acquiring the plurality of partial k-space data by the reconstruction function 129f, A reference image may be generated by applying a reconstruction process to the acquired k-space data after acquiring a plurality of partial k-space data.

In addition, the processing circuit 129 may create a reference image by applying reconstruction processing to k-space data acquired before or after a contrast examination using the reconstruction function 129f. For example, a reference image for k-space data acquired before a contrast-enhanced exam is used to create the first half of the time-series frames in the contrast-enhanced exam. In addition, the reference image related to the k-space data acquired after the contrast examination is used when creating the latter half of the time-series frames in the contrast examination.

The processing circuit 129 generates a plurality of synthesized images by synthesizing the reference image and each of the plurality of difference images using a synthesized image generation function 129g. Specifically, processing circuitry 129 generates a plurality of composite images by simply adding the reference image and each of the plurality of difference images. Simple addition corresponds to, for example, adding pixel values at the same pixel position in a plurality of images.

FIG. 8 is a flowchart illustrating processing performed by processing circuitry in one embodiment. The processing in FIG. 8 is started by the processing circuit 129 executing a program relating to the differential reconstruction processing, for example, when a user or the like inputs an instruction to start the differential reconstruction processing.

In the differential reconstruction process, a differential image is generated by applying a reconstruction process to differential k-space data obtained by subtracting reference k-space data based on a reference image from acquired time-series k-space data, and generating a differential image. This is a process of generating a reconstructed image by adding a reference image to . By performing the differential reconstruction process, it is expected that the reconstructed image will have a higher image quality by reconstructing the differential k-space data than by reconstructing the time-series k-space data as it is.

In the following description, it is assumed that partial k-space data corresponding to each of a plurality of frames is acquired in advance by the imaging control circuit 121. FIG.

(Step S801)
The processing circuit 129 generates reference k-space data by applying the inverse transformation of the reconstruction processing to the reference image using the reference k-space data generation function 129c.

(Step S802)
The processing circuit 129 uses a reference partial k-space data generation function 129d to generate filling positions and reference data so that the data filling positions in the k-space are different and correspond to a plurality of partial k-space data relating to a plurality of frames along the time series. A plurality of reference partial k-space data are generated based on the k-space data.

(Step S803)
The processing circuit 129 generates a plurality of differential k-space data by executing a difference between a plurality of partial k-space data and a plurality of reference k-space data for each of a plurality of frames by a differential k-space data generation function 129e. Generate.

(Step S804)
The processing circuit 129 generates a plurality of difference images by applying a reconstruction process to each of the plurality of difference k-space data by the reconstruction function 129f.

(Step S805)
The processing circuit 129 generates a plurality of synthesized images by synthesizing the reference image and each of the plurality of difference images using a synthesized image generation function 129g.

FIG. 9 is a diagram illustrating a specific example of reconstruction processing in one embodiment. In FIG. 9, the reference image 930 is generated from the k-space data 920 obtained by integrating all of the partial k-space data 910, but the present invention is not limited to this. Also, when PI is used, the reconstruction processing shown in FIG. 9 is performed for each of a plurality of receiving coils.

The imaging control circuit 121 acquires a plurality of partial k-space data 910 . Plurality of partial k-space data 910 includes first partial k-space data 911 , second partial k-space data 912 , and third partial k-space data 913 . Each of the plurality of partial k-space data 910 has a different data filling position in the k-space.

Processing circuitry 129 generates reference image 930 by applying reconstruction processing to k-space data 920 by reconstruction function 129f.

The processing circuit 129 generates the reference k-space data 940 by applying the inverse transformation of the reconstruction processing to the reference image 930 by the reference k-space data generation function 129c.

The processing circuit 129 generates first partial k-space data 911, second partial k-space data 912, and third partial k-space data regarding a plurality of frames in time series by a reference partial k-space data generation function 129d. A plurality of reference partial k-space data 950 are generated based on the fill positions and the reference k-space data 940 , each corresponding to 913 .

The filling position can be specified, for example, by obtaining the spoke combination corresponding to an arbitrary frame by referring to the correspondence table 700 shown in FIG. The plurality of reference portion k-space data 950 includes first reference portion k-space data 951 , second reference portion k-space data 952 and third reference portion k-space data 953 .

The processing circuit 129 generates a plurality of differential k-space data by executing the difference between the plurality of partial k-space data 910 and the plurality of reference partial k-space data 950 for each of the plurality of frames by the differential k-space data generation function 129e. Spatial data 960 is generated.

Specifically, processing circuitry 129 generates first differential k-space data 961 that is the difference between first partial k-space data 911 and first reference partial k-space data 951 for the first frame. Processing circuitry 129 produces second differential k-space data 962 that is the difference between second partial k-space data 912 and second reference partial k-space data 952 for the second frame. Processing circuitry 129 produces third differential k-space data 963 that is the difference between third partial k-space data 913 and third reference partial k-space data 953 for the third frame.

The processing circuit 129 generates a plurality of differential images 970 by applying reconstruction processing to the plurality of differential k-space data 960 by the reconstruction function 129f.

Specifically, processing circuitry 129 generates first difference image 971 by applying a reconstruction process to first difference k-space data 961 . Processing circuitry 129 generates second difference image 972 by applying a reconstruction process to second difference k-space data 962 . Processing circuitry 129 generates third difference image 973 by applying a reconstruction process to third difference k-space data 963 .

The processing circuit 129 generates a plurality of synthesized images 980 by synthesizing the reference image 930 and each of the plurality of difference images 970 using the synthesized image generation function 129g.

Specifically, the processing circuit 129 generates a first synthetic image 981 by synthesizing the reference image 930 and the first difference image 971 . Processing circuitry 129 generates second composite image 982 by combining reference image 930 and second difference image 972 . Processing circuitry 129 generates third composite image 983 by combining reference image 930 and third difference image 973 .

As described above, according to one embodiment, the MRI apparatus generates reference k-space data by applying the inverse transformation of the reconstruction process to the reference image, and the filling positions in the k-space are different and chronologically. A plurality of reference partial k-space data are generated based on the fill positions and the reference k-space data to respectively correspond to a plurality of partial k-space data for a plurality of frames along the method. Then, the MRI apparatus generates a plurality of differential k-space data by performing differences between the plurality of partial k-space data and the plurality of reference k-space data for each of the plurality of frames, and generates a plurality of differential k-space data. A plurality of difference images are generated by applying reconstruction processing to the data, respectively, and a plurality of synthesized images are generated by synthesizing the reference image and each of the plurality of difference images. Therefore, since the MRI apparatus applies the reconstruction processing to the difference k-space data whose data acquisition amount is smaller than that of the partial k-space data, it is possible to reduce the number of computations required for convergence of iterative computations in the reconstruction processing. I can expect it. In addition, by synthesizing the reference image and the difference image, the MRI apparatus can improve the image quality of the reconstructed image with a smaller number of iterations than by applying the reconstruction process directly to the partial k-space data. I can expect it.

Also, according to one embodiment, the MRI apparatus generates a plurality of reference partial k-space data by extracting k-space trajectories respectively corresponding to filling positions from the reference k-space data. That is, the MRI apparatus can acquire k-space data corresponding to the filling position of the partial k-space data.

Also, according to one embodiment, the MRI apparatus generates a reference image by applying reconstruction processing to k-space data obtained by integrating all of a plurality of partial k-space data. That is, the MRI apparatus can obtain a reference image close to the Nyquist rate.

Further, according to one embodiment, the MRI apparatus generates a reference image by applying reconstruction processing to k-space data obtained by integrating at least two pieces of partial k-space data. . That is, the MRI apparatus can acquire the reference image without waiting for the time to acquire all of the partial k-space data.

Also, according to one embodiment, the MRI apparatus generates a reference image by applying a reconstruction process to the acquired k-space data before acquiring the plurality of partial k-space data. That is, the MRI apparatus can acquire the reference image before generating the reference image based on the multiple partial k-space data.

Also, according to one embodiment, the MRI apparatus generates a reference image by applying a reconstruction process to the acquired k-space data after acquiring a plurality of partial k-space data. That is, the MRI apparatus can acquire reference images based on different scan sequences for the same imaging region.

Also, according to one embodiment, the MRI apparatus generates a reference image by applying reconstruction processing to k-space data acquired before or after a contrast examination. That is, the MRI apparatus can obtain a reference image that is not affected by the contrast agent in the same imaging region.

Therefore, according to the MRI apparatus 100 of the embodiment described above, it is possible to improve the image quality of the magnetic resonance image within the restricted processing time.

It should be noted that, in one embodiment, acquisition of partial k-space data mainly by radial scanning has been described, but the present invention is not limited to this. For example, multiple partial k-space data may be acquired by spiral scanning with multiple interleaves. Interleaving corresponds to, for example, one of the spiral collection lines in spiral scanning. When using spiral scanning, for example, each of the plurality of partial k-space data becomes k-space data acquired by one interleaving. Also for example, each of the plurality of partial k-space data may be acquired by a Cartesian scan based on acquisition lines of pseudo-randomly generated fill locations.

(Modification)
As a modified example of one embodiment, when the technical concept of the differential reconstruction processing of the MRI apparatus 100 is realized by the processing apparatus 131, the configuration of FIG. At this time, the interface 123 functions, for example, as a circuit related to a communication interface used for connection with a network, and acquires partial k-space data and the like. Specifically, the interface 123 acquires partial k-space data and the like from the MRI apparatus via the network. In this modified example, the interface 123 is an example of an acquisition unit.

In the processing device 131, the processing circuit 129 generates reference k-space data by applying the inverse transformation of the reconstruction processing to the reference image using the reference k-space data generation function 129c. The processing circuit 129 uses a reference partial k-space data generation function 129d to generate filling positions and reference data so that the data filling positions in the k-space are different and correspond to a plurality of partial k-space data relating to a plurality of frames along the time series. A plurality of reference partial k-space data are generated based on the k-space data. The processing circuit 129 generates a plurality of differential k-space data by executing a difference between a plurality of partial k-space data and a plurality of reference k-space data for each of a plurality of frames by a differential k-space data generation function 129e. Generate. The processing circuit 129 generates a plurality of difference images by applying a reconstruction process to each of the plurality of difference k-space data by the reconstruction function 129f. The processing circuit 129 generates a plurality of synthesized images by synthesizing the reference image and each of the plurality of difference images using a synthesized image generation function 129g. Note that the differential reconstruction processing by the processing circuit 129 of the processing device 131 is the same as the processing in one embodiment, so the description is omitted.

According to the processing device 131 of the modified example described above, it is possible to improve the image quality of the magnetic resonance image within the restricted processing time.

The operations of the MRI apparatus 100 of the embodiment and the processing apparatus 131 of the modified example described above can be executed as a medical image processing method. Effects related to the medical image processing method are the same as those in the above-described embodiment and modified examples, and thus description thereof is omitted.

According to at least one embodiment described above, it is possible to improve the image quality of magnetic resonance images in a limited processing time.

While several embodiments of the invention have been described, these embodiments have been presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, as well as the scope of the invention described in the claims and equivalents thereof.

100 magnetic resonance imaging apparatus 101 static magnetic field magnet 103 gradient magnetic field coil 107 bed 107a table top 111 bore 115 transmission coil 117 reception coil 131 processing device 200, 920 k-space data 300, 911 first partial k-space data 400, 912 second partial k-space data 500, 913 third partial k-space data 700 correspondence table 910 plural partial k-space data 930 reference image 940 reference k-space data 950 plural reference partial k-space data 951 first reference partial k-space data 952 Second reference partial k-space data 953 Third reference partial k-space data 961 First difference k-space data 962 Second difference k-space data 963 Third difference k-space data 971 First difference image 972 2 difference image 973 third difference image 981 first composite image 982 second composite image 983 third composite image

Claims (14)

an inverse transform unit that generates reference k-space data by applying the inverse transform of the reconstruction process to the reference image;
A plurality of reference partial k-spaces based on the filling positions and the reference k-space data so that data filling positions in the k-space are different and correspond to a plurality of partial k-space data relating to a plurality of frames along the time series. a generator that generates data;
a differentiator that generates a plurality of differential k-space data by performing a difference between the plurality of partial k-space data and the plurality of reference partial k-space data for each of the plurality of frames;
a reconstruction unit that generates a plurality of difference images by applying the reconstruction processing to the plurality of difference k-space data, respectively;
A magnetic resonance imaging apparatus comprising: a synthesizing unit that generates a plurality of synthesized images by synthesizing the reference image and each of the plurality of difference images. 2. The magnetic resonance imaging apparatus according to claim 1, wherein said generator generates said plurality of partial reference k-space data by extracting k-space trajectories respectively corresponding to said filling positions from said reference k-space data. 2. The reconstruction unit generates the reference image by applying the reconstruction processing to k-space data obtained by integrating at least two pieces of the partial k-space data. Or the magnetic resonance imaging apparatus according to claim 2. 3. The magnetic resonance imaging apparatus according to claim 1, wherein said reference k-space data is k-space data acquired before acquiring said plurality of partial k-space data. 3. The reconstruction unit according to claim 1, wherein the reconstruction unit generates the reference image by applying the reconstruction processing to k-space data acquired after acquiring the plurality of partial k-space data. magnetic resonance imaging equipment. 3. Magnetic resonance imaging according to claim 1, wherein said reconstruction unit generates said reference image by applying reconstruction processing to k-space data acquired before or after a contrast examination. Device. an acquisition unit that acquires the plurality of partial k-space data,
7. The magnetic resonance imaging apparatus according to any one of claims 1 to 6 , wherein said acquisition unit performs radial scanning to acquire said plurality of partial k-space data. an acquisition unit that acquires the plurality of partial k-space data,
7. The magnetic resonance imaging apparatus according to any one of claims 1 to 6 , wherein said acquisition unit performs spiral scanning to acquire said plurality of partial k-space data. an acquisition unit that acquires the plurality of partial k-space data,
7. The magnetic resonance imaging apparatus according to any one of claims 1 to 6 , wherein said acquisition unit performs Cartesian scanning to acquire said plurality of partial k-space data. 10. The magnetic field according to any one of claims 1 to 9 , wherein the reconstruction unit generates the plurality of difference images by introducing correlations between the plurality of frames into the reconstruction process. Resonance imaging device. 11. The magnetic resonance imaging apparatus of claim 10 , wherein said correlation is compression sensing. 12. The magnetic resonance according to any one of claims 1 to 11 , wherein said synthesizing unit generates said plurality of synthesized images by simply adding said reference image and each of said plurality of difference images. imaging device. an inverse transform unit that generates reference k-space data by applying the inverse transform of the reconstruction process to the reference image;
A plurality of reference partial k-spaces based on the filling positions and the reference k-space data so that data filling positions in the k-space are different and correspond to a plurality of partial k-space data relating to a plurality of frames along the time series. a generator that generates data;
a differentiator that generates a plurality of differential k-space data by performing a difference between the plurality of partial k-space data and the plurality of reference partial k-space data for each of the plurality of frames;
a reconstruction unit that generates a plurality of difference images by applying the reconstruction processing to the plurality of difference k-space data, respectively;
a synthesizing unit that generates a plurality of synthetic images by synthesizing the reference image and each of the plurality of difference images. generating reference k-space data by applying the inverse transform of the reconstruction process to the reference image;
A plurality of reference partial k-spaces based on the filling positions and the reference k-space data so that data filling positions in the k-space are different and correspond to a plurality of partial k-space data relating to a plurality of frames along the time series. generating data;
generating a plurality of differential k-space data by performing a difference between the plurality of partial k-space data and the plurality of reference partial k-space data for each of the plurality of frames;
generating a plurality of difference images by respectively applying the reconstruction processing to the plurality of difference k-space data;
Generating a plurality of synthesized images by synthesizing the reference image and each of the plurality of difference images.

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